Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26199
Title: Alleviating 'overfitting' via genetically-regularised neural network
Authors: Chan, ZSH
Ngan, HW
Rad, AB
Ho, TK
Issue Date: 2002
Publisher: Institution of Engineering and Technology
Source: Electronics letters, 2002, v. 38, no. 15, p. 809-810 How to cite?
Journal: Electronics letters 
Abstract: A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN 'over-fitting'. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results.
URI: http://hdl.handle.net/10397/26199
ISSN: 0013-5194
EISSN: 1350-911X
DOI: 10.1049/el:20020592
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
0
Citations as of Aug 6, 2018

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Aug 16, 2018

Page view(s)

71
Last Week
0
Last month
Citations as of Aug 12, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.